library(foreign)
library(tidyverse)
library(knitr)
library(DT)
library(tigris)
library(stringr)
library(ggplot2) #development version
source("theme_nothing.R")
data(fips_codes)
county_names <- fips_codes
county_names$fips <- as.numeric(paste0(county_names$state_code, county_names$county_code))
county_names$county_name <- gsub(" County.*", "", county_names$county)
county_names$county_name <- paste0(county_names$county_name, ", ", county_names$state)
county_names <- select(county_names, fips, county_name)
data <- read.spss("data/SHR1976_2015.sav", to.data.frame=TRUE)
data2 <- read.spss("data/SHR1976_2015.sav", to.data.frame=TRUE, use.value.labels=F)
data <- select(data,
ID, CNTYFIPS, Ori, State, Agency, AGENCY_A,
Agentype_label=Agentype,
Source_label=Source,
Solved_label=Solved,
Year,
Month_label=Month,
Incident, ActionType,
Homicide_label=Homicide,
Situation_label=Situation,
VicAge,
VicSex_label=VicSex,
VicRace_label=VicRace,
VicEthnic, OffAge,
OffSex_label=OffSex,
OffRace_label=OffRace,
OffEthnic,
Weapon_label=Weapon,
Relationship_label=Relationship,
Circumstance_label=Circumstance,
Subcircum, VicCount, OffCount, FileDate,
fstate_label=fstate,
MSA_label=MSA,
StateName2)
data2 <- select(data2,
Agentype_value=Agentype,
Source_value=Source,
Solved_value=Solved,
Month_value=Month,
Homicide_value=Homicide,
Situation_value=Situation,
VicSex_value=VicSex,
VicRace_value=VicRace,
OffSex_value=OffSex,
OffRace_value=OffRace,
Weapon_value=Weapon,
Relationship_value=Relationship,
Circumstance_value=Circumstance,
fstate_value=fstate,
MSA_value=MSA)
data <- cbind(data, data2)
data <- select(data,
ID, CNTYFIPS, Ori, State, Agency, AGENCY_A,
Agentype_label, Agentype_value,
Source_label, Source_value,
Solved_label, Solved_value,
Year,
Month_label, Month_value,
Incident, ActionType,
Homicide_label,Homicide_value,
Situation_label,Situation_value,
VicAge,
VicSex_label,VicSex_value,
VicRace_label,VicRace_value,
VicEthnic, OffAge,
OffSex_label,OffSex_value,
OffRace_label,OffRace_value,
OffEthnic,
Weapon_label,Weapon_value,
Relationship_label,Relationship_value,
Circumstance_label,Circumstance_value,
Subcircum, VicCount, OffCount, FileDate,
fstate_label,fstate_value,
MSA_label,MSA_value,
StateName2)
data$CNTYFIPS <- as.character(data$CNTYFIPS)
data$CNTYFIPS <- ifelse(data$CNTYFIPS=="51560", "51005", data$CNTYFIPS)
data$CNTYFIPS <- ifelse(data$CNTYFIPS=="02232", "02105", data$CNTYFIPS)
data$CNTYFIPS <- ifelse(data$CNTYFIPS=="02280", "02195", data$CNTYFIPS)
data$CNTYFIPS <- ifelse(data$CNTYFIPS=="02201", "02198", data$CNTYFIPS)
data$CNTYFIPS <- ifelse(data$CNTYFIPS=="02201", "02198", data$CNTYFIPS)
data$fips <- as.numeric(data$CNTYFIPS)
data <- left_join(data, county_names)
data <- mutate(data,
solved_num = ifelse(Solved_label=="Yes", 1, 0),
agegroup_label = ifelse(VicAge %in% 0:14, "0-14",
ifelse(VicAge %in% 15:19, "15-19",
ifelse(VicAge %in% 20:50, "20-50",
ifelse(VicAge %in% 51:99, "Over 50", "Unknown")))),
agegroup_value = ifelse(VicAge %in% 0:14, 1,
ifelse(VicAge %in% 15:19, 2,
ifelse(VicAge %in% 20:50, 3,
ifelse(VicAge %in% 51:99, 4, 9)))),
sex = ifelse(VicSex_label=="Male", 1,
ifelse(VicSex_label=="Female", 2, 9)),
murdgrp_msa=(MSA_value*10000)+(sex*1000)+(agegroup_value*100)+Weapon_value,
murdgrp_cnty=(as.numeric(as.character(CNTYFIPS))*10000)+(sex*1000)+(agegroup_value*100)+Weapon_value)
murdergroup_msa <- data %>%
group_by(murdgrp_msa, agegroup_label, VicSex_label, MSA_label, Weapon_label) %>%
# filter(Year>=2000) %>%
summarize(total=n(), solved=sum(Solved_value, na.rm=T)) %>%
mutate(percent=round(solved/total*100,2))
#datatable(murdergroup_msa)
murdergroup_cnty <- data %>%
group_by(murdgrp_cnty, agegroup_label, VicSex_label, county_name, Weapon_label) %>%
summarize(total=n(), solved=sum(Solved_value)) %>%
mutate(percent=round(solved/total*100,2))
#datatable(murdergroup_cnty)
Clusters by County
spots_cnty <- murdergroup_cnty %>%
filter(VicSex_label=="Female", murdgrp_cnty>0, percent <=33) %>%
mutate(unsolved=total-solved) %>%
arrange(desc(unsolved))
datatable(spots_cnty)
Clusters by metro areas
spots_msa <- murdergroup_msa %>%
filter(VicSex_label=="Female", murdgrp_msa>0, percent <=33) %>%
mutate(unsolved=total-solved) %>%
arrange(desc(unsolved))
datatable(spots_msa)
Clusters by County since 2006
spots_cnty_2006 <- data %>%
group_by(murdgrp_cnty, agegroup_label, VicSex_label, county_name, Weapon_label, CNTYFIPS) %>%
filter(Year>=2006) %>%
summarize(total=n(), solved=sum(Solved_value)) %>%
mutate(percent=round(solved/total*100,2)) %>%
filter(VicSex_label=="Female", murdgrp_cnty>0, percent <=33) %>%
mutate(unsolved=total-solved) %>%
filter(unsolved!=1) %>%
arrange(desc(unsolved))
datatable(spots_cnty_2006)
counties_map <- counties(cb = FALSE, resolution = "500k", year = NULL)
counties_map <- subset(counties_map, STATEFP!="78" & STATEFP!="72" & STATEFP!="66" & STATEFP!="60" & STATEFP!="69" & STATEFP!="02" & STATEFP!="15")
cm_fort <- fortify(counties_map, region="GEOID")
colnames(spots_cnty_2006)[colnames(spots_cnty_2006) == 'CNTYFIPS'] <- 'id'
spots_cnty_2006$id <- str_trim(spots_cnty_2006$id)
cm_fort_spots <- left_join(cm_fort, spots_cnty_2006)
cm_fort_spots <- filter(cm_fort_spots, !is.na(unsolved))
#cm_fort_spots_2050 <- filter(cm_fort_spots, agegroup_label=="20-50")
#cm_fort_spots_2050_s <- filter(cm_fort_spots_2050, Weapon_label=="Strangulation - hanging")
age_groups <- unique(cm_fort_spots$agegroup_label)
weapons <- unique(cm_fort_spots$Weapon_label)
for (i in 1:length(age_groups)) {
for (x in 1:length(weapons)) {
cm_fort_spots_sub <- filter(cm_fort_spots, agegroup_label==age_groups[i] & Weapon_label==weapons[x])
counties_m <- ggplot()
counties_m <- counties_m + geom_polygon(data=cm_fort, aes(x=long, y=lat, group=group), fill=NA, color="black", size=0.05)
counties_m <- counties_m + geom_polygon(data=cm_fort_spots_sub, aes(x=long, y=lat, group=group, fill=unsolved), color="black", size=0.2)
counties_m <- counties_m + coord_map("albers",lat0=39, lat1=45)
#counties_m <- counties_m + facet_wrap(agegroup_label~Weapon_label, ncol=2)
#counties_m <- counties_m + scale_fill_gradient2(low=muted("purple"), high=muted("green"), label=dollar, name=NULL, breaks=c(-9000,0,3000))
counties_m <- counties_m + theme_nothing(legend=TRUE)
counties_m <- counties_m + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
counties_m <- counties_m + theme(strip.background = element_blank(),
strip.text.y = element_blank())
counties_m <- counties_m + labs(x=NULL, y=NULL, title=weapons[x], subtitle=age_groups[i])
counties_m <- counties_m + theme(plot.title=element_text(face="bold", hjust=0))
counties_m <- counties_m + theme(plot.subtitle=element_text(face="italic", size=9, margin=margin(l=20)))
counties_m <- counties_m + theme(plot.caption=element_text(size=12, margin=margin(t=12), color="#7a7d7e", hjust=0))
counties_m <- counties_m + theme(legend.key.size = unit(1, "cm"))
counties_m <- counties_m + theme(legend.position="top")
print(counties_m)
#counties_m <- counties_m + theme(strip.text.x = element_text(size=12, face="bold"))
#ggsave(paste0(age_groups[i],"_",weapons[x], ".png"))
#print(paste0(age_groups[i],"_",weapons[x], ".png"))
}
}
